---
title: Overview
description: Quickest way to perform evaluations on your data
---

UpTrain provides a simple and easy way to perform evaluations on your data. You can pass any of these Evals to the `evaluate` function in `EvalLLM` class and it will automatically perform the evaluation.

These evals require a combination of the following columns to be present in your data:

- `question`: The question you want to ask
- `context`: The context relevant to the question
- `response`: The response to the question

Some evals may require additional parameters to be passed to them. These are called parametric evals. Any eval below that has a `Parameters` section is a parametric eval.

You can choose evals as per your needs. We have divided them into a few categories for your convenience:

<AccordionGroup>
  <Accordion title="Ground Truth Comparison Evals">
| Eval | Description |
| ---- | ----------- |
|[Response Matching](/predefined-evaluations/ground-truth-comparison/response-matching) | Grades how relevant the generated context was to the question specified. |
  </Accordion>

  <Accordion title="Response Quality Evals">
| Eval | Description |
| ---- | ----------- |
|[Reponse Completeness](/predefined-evaluations/response-quality/response-completeness) | Grades whether the response has answered all the aspects of the question specified. |
|[Reponse Conciseness](/predefined-evaluations/response-quality/response-conciseness) | Grades how concise the generated response is or if it has any additional irrelevant information for the question asked. |
|[Reponse Relevance](/predefined-evaluations/response-quality/response-relevance)| Grades how relevant the generated context was to the question specified.|
|[Reponse Validity](/predefined-evaluations/response-quality/response-validity)| Grades if the response generated is valid or not. A response is considered to be valid if it contains any information.|
|[Reponse Consistency](/predefined-evaluations/response-quality/response-consistency)| Grades how consistent the response is with the question asked as well as with the context provided.|
  </Accordion>

  <Accordion title="Context Awareness Evals">
| Eval | Description |
| ---- | ----------- |
|[Context Relevance](/predefined-evaluations/context-awareness/context-relevance) | Grades how relevant the context was to the question specified. |
|[Context Utilization](/predefined-evaluations/context-awareness/context-utilization) | Grades how complete the generated response was for the question specified given the information provided in the context. |
|[Factual Accuracy](/predefined-evaluations/context-awareness/factual-accuracy)| Grades whether the response generated is factually correct and grounded by the provided context.|
|[Context Conciseness](/predefined-evaluations/context-awareness/context-conciseness)| Evaluates the concise context cited from an original context for irrelevant information.
|[Context Reranking](/predefined-evaluations/context-awareness/context-reranking)| Evaluates how efficient the reranked context is compared to the original context.|
  </Accordion>

  <Accordion title="Security Evals">
| Eval | Description |
| ---- | ----------- |
|[Prompt Injection](/predefined-evaluations/safeguarding/prompt-injection) | Grades whether the generated response is leaking any system prompt. |
|[Jailbreak Detection](/predefined-evaluations/safeguarding/jailbreak) | Grades whether the user's prompt is an attempt to jailbreak (i.e. generate illegal or harmful responses). |
  </Accordion>

  <Accordion title="Language Quality Evals">
| Eval | Description |
| ---- | ----------- |
|[Language Features](/predefined-evaluations/language-quality/fluency-and-coherence) | Grades whether the response has answered all the aspects of the question specified. |
|[Tonality](/predefined-evaluations/language-quality/tonality) | Grades whether the generated response matches the required persona's tone  |
  </Accordion>

    <Accordion title="Query Clarity Evals">
| Eval | Description |
| ---- | ----------- |
|[Sub-query Completeness](/predefined-evaluations/query-quality/sub-query-completeness) | Evaluate if the list of generated sub-questions comprehensively cover all aspects of the main question. |
|[Multi-query Accuracy](/predefined-evaluations/query-quality/multi-query-accuracy) | Evaluates how accurately the variations of the query represent the same question. |
  </Accordion>

  <Accordion title="Code Related Evals">
| Eval | Description |
| ---- | ----------- |
|[Code Hallucination](/predefined-evaluations/code-evals/code-hallucination) | Grade whether the code present in the generated response is grounded by the context. |
  </Accordion>

  <Accordion title="Conversation Evals">
| Eval | Description |
| ---- | ----------- |
|[User Satisfaction](/predefined-evaluations/conversation-evals/user-satisfaction) | Grade the conversations between the user and the LLM/AI assistant. |
  </Accordion>

  <Accordion title="Creating Custom Evals">
| Eval | Description |
| ---- | ----------- |
|[Custom Guideline](/predefined-evaluations/custom-evals/custom-guideline) | Grades how well the LLM adheres to a provided guideline when giving a response. |
|[Custom Prompts](/predefined-evaluations/custom-evals/custom-prompt-eval) | Allows you to create your own set of evaluations. |
  </Accordion>


  
</AccordionGroup>